Increased M&A Activity in the AI Space: PitchBook Report Recap

According to Pitchbook, recent data shows a sharp increase in the number of strategic acquisition deals from major tech giants of small tactical AI companies. There are multiple reasons why a giant would want to mass acquire startups, the primary of which is adding capital whether that comes in the form of a solid working team with subject matter expertise or in the form of assets held by the target. 

More nuanced than that, many acquisitions come from an offensive play in the market to take a small target company off the market to prevent competitors from entering a new space. Especially with AI, which in regulatory terms is quite new, companies can feel free to play around with the market with little to no consequence. The M&A activity reflects the mindset of these giants to onboard new teams rather than develop their pre-existing teams to be able to handle new issues. 

Major acquirers have been increasing activity in 3 specific areas of AI tech: Core AI, Consumer AI, and National Language Technology. The distribution of acquisitions across these spaces is dependent mostly on the target’s compatibility. For example, Apple invests more heavily in Consumer AI than other companies in FAMGA. The primary reason for this is Apple moving more closely to analyzing macro data for machine learning in the context of creating new applications for their consumers. 

As an investment strategy, a firm could almost develop an arbitrage strategy given a specific acquirer in mind. For example, if a firm decides that Apple is collecting a lot more consumer data than they are able to efficiently analyze and turn into actionable products, the firm should decide to invest in multiple consumers AI startups with the hope that Apple will acquire the startup. 

The Nature of AI Acquisitions

The nature of AI data analytics is uniquely strategic in terms of acquisitions. To elaborate, AI is meant to quickly analyze mass amounts of unstructured data without massive data cleaning, organization, or formatting. As such, it makes for the perfect time-saving acquisition that most FAMGA type organizations look for. 

These companies have the technical and human resources to create these types of products but just out of the cost heavy nature of product management, agile development, and computer scientist’s salaries, it often makes more sense to acquire smaller startups who are able to save time and capital in a massive way. 

Thinking about it from a business perspective, Apple’s acquisition of a consumer AI company may yield 2-3 industry differentiating features that in turn increases sales. To a mega-giant like Apple, their product is so popular in the market that to acquire even a sustainable 1% growth in sales is so significant that it is worth many millions of dollars of acquisitions. 

As investors, it is important to keep in mind that many of these companies are not solely looking for time-saving targets. Antitrust laws haven’t been updated well across tech spaces especially due to the fact that Tech is a sector that moves a lot quicker than legislation does. Antitrust laws still apply in large to Major tech companies due to the fact that many of them formed as groups of hundreds of acquisitions over the course of the decade.

Big Tech has been known for making large-scale acquisitions in order to enhance their product or to take out the competition. The largest acquisitions of this nature have come from the autonomous vehicle space and the semiconductor space. 

Understanding that AI technology may be a new space with large-scale acquisitions could be an exciting prospect for private Equity investors as it allows them to differentiate their current portfolios, and add significantly desirable Investments for their investors.

Shaping Future Deals

The use of AI technology is a break from traditional private equity investment strategies in that many of these companies are not majorly profitable, have large-scale operational capabilities that need optimization, and do not necessarily have a very obvious or multitudinous payout strategy. 

This makes it difficult for private equity firms to use old models to value AI companies. This balance can be struck best by analyzing AI companies through the lens of Industry comparables instead of evaluating based on EBITDA or revenue multiples.

Overall, the market is trending towards using more AI technology as the mass amount of data analytics and regulatory inefficiencies come from this space. This creates an exciting addition to most PE portfolios because PE firms are now going to be able to utilize many new AI Technologies and their other Investments. 

Process optimization is very common and simple as an AI Tech and would help many other non-technical Investments increase their productivity and in turn, their valuation.

Buyers Guide 2.0

A traditional crm was built for general ‘customer’ scenarios

Software platforms have made the world a better place by making work a better place. Indeed the world is better off when people enjoy their jobs even marginally more, and workplace applications on big CRM platforms like have done that and much more.

But the potential that platforms like these offer presents diminishing returns: once the platform provider has engineered too many industry specific components into its platform, its usefulness for other industries begins to be threatened, and with that so do the usefulness of the component tools built into the platform.

So it is with the CRM category that has defined: it is generic enough to work for many industries, and yet still offers the potential for others to round off the edges and nail more vertically-oriented and extremely tailored software solutions.

Private capital markets are actually a great demonstration of this dynamic. Where generic CRM platforms simplify — appropriately so — to assume there’s a business, a customer, a sale, and service of that customer, there are a few industry-specific pieces that are missing.

Take for example, that investors become customers by investing through legal entities the GP raises. It’s a subtle but important nuance that just doesn’t make sense at a platform-as-a-service level (because it’s overly complicated for a simple one-time sale that many industries require), but which can easily be added without 10 years or software engineering. Once provided, the rest of the platform’s components become tremendously powerful again and you’re set to take over the world.

As a traditional CRM in our pillars methodology, these nuances must be present to properly account for investors in these legal entities, potential target companies and which are owned by these entities, the context of all interactions with these parties (as well as the appropriate overlap, ie co-investments), and how you’re arriving at finding these opportunities on both sides of the equation, such that you’re able to piece together what’s effective and what’s not. Not just because we say so, but because these are the very relationships and data that are key to the motivation behind a CRM in any industry.

It’s critical, too, that the valuable publicly-available information that helps to enrich CRM systems and save users painful steps of entering it themselves is fully-integrated at the platform level.

Again, look no further than the 3,000+ pre-built integrations that — the creator of the CRM platform concept — has at a platform level to do so, and which only exists by way of holding just short of overly-specifying certain industry workflows that would present challenges to properly integrate.

Stakeholder reporting and communication (investor relations) draws on a range of datasets

The traditional “customer service” model of CRM systems once again makes overly-simplified assumptions about the customer relationship when applied to private capital markets.

In fifteen years I personally have yet to hear the terms “warranty” or “service call” in this market because it’s just not the same. But make no mistake, as uncomfortable as it may be to say aloud, customer service is more important now than ever and it’s constantly happening; the industry is, after all, considered to be a financial “service”.

As it turns out, that service is primarily information-based — it’s driven by data and takes the form of reports and analysis that drive decisions, and then end up again in investor-facing reports and analysis.

The foundational elements of a private capital markets CRM must be built such that they accommodate this data (like we discussed above), but so too that it can accommodate additional supporting data that investors (customers!) need in the context of service.

Oftentimes this supporting data — financial metrics and time-based values, for example — is believed not to meet the traditional definition of CRM and the natural thought is “well, better do this in Excel!”.

While I happen to believe Excel is still the greatest software application ever built, its introduction to this value chain we’ve discussed herein actually creates the problem many firms suffer from: key data needed to provide customer service (again: effectively the entirety of a firm’s reports and analysis) is now in disparate systems and detached.

Both of those dynamics are important and distinct: not only is this supplemental data disparate, but when brought together there is no logical association that can be made between the two data sets.

Allow me, then, to make the point very simply: not only can this financial and time-based value data (you may be thinking about is as “portfolio monitoring” or “accounting”) be a part of a CRM, it is arguably the most important part of a CRM because it’s at the core of what providing service to the customer entails — information that comes out of data!

Firms need a digital method to engage stakeholders (ie investor portals)

Investor portals are not new; in fact, for many of us — including myself — they conjure up horrifying nightmares in which we’re aimlessly guessing at folders to find the newest document we need.

So in lies the opportunity: not only have the portals we’ve come to hate not simplified the process of acquiring information, they’ve failed to create an entirely new experience that is “customer service” driven.

To be fair, this is not a B2C market where you’d be long out of business for not having focused on customer service and thus the customer’s technology-driven experience. But don’t expect to be around too much longer if you aren’t thinking about this shift.

Today’s institutional investors increasingly expect this same consumer-like experience, and a massive opportunity is being missed by not providing it. It’s not about providing them the experience they desire; it’s more about the ability to measure engagement that is had in return.

Put simply: what’s keeping the market from providing this experience is the availability of the information that’s required to create the service that provides the experience.

If you’ve hung in this long, you know that by focusing on your CRM, you have the data that’s required to manage the customer relationship and the technology-driven experience through which that information is shared to create a differentiated and opportunistic customer experience.